I'm using all FREE 100% AI Open Source Models

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Here is a list of my top open source models

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🚀 2026 Guide: The Best New Open-Source & Free LLMs — Run Massive Models for Zero Cost

Listen, we’ve got another one for you — and honestly, this should’ve been talked about already. But the reason I’m speaking on it isn’t because it’s trending… it’s from actually building and using this stuff.

Let’s talk about using real free LLMs outside of Ollama or just downloading models locally — especially when you’re dealing with an older laptop (like I am right now).

From real experience building, you have to test with actual LLMs. And even though some are “free,” a lot of them are:

Rate-limited
Come with restrictions
Or have serious issues with tool calling and long runs
So what happens? You end up wasting tokens dealing with errors, retries, and constant fixes.

I got to the point where I was like — I need something better just for testing.

So I built a hosting setup… but then ran into another problem:
On free tiers (like Amazon Web Services and others), you get:

Weak GPU performance
Slow response times
Connection latency issues
So what does that really mean?

You actually need:

A solid Wi-Fi connection
A decent laptop
And proper infrastructure
just to reliably test or host larger models…

THE OPEN SOURCE MODEL WAVE IS HERE
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The open source wave is here; the cost and availability of Artificial Intelligence will be a new norm in society if you ask me.

Scary thing though is like what is next from it or as it grows???

I'ma say again and shout out @petersteinberger and #openclaw. Why, because to me this project brought a lot of needed attention to Open Source, for the average person to know about and learn. How many times, 5 years ago, did you ever hear of Open Source (to my non-developers)?

Now with China also outdoing America every chance they drop a new model lol, this causes, like to me, a saturation in the market to where these big companies have to give more free access, to gain, let's say, more exposure, users, and data.

The easiest and most used open source model provider and connector in this playing field is Ollama. But Ollama just gives access to these models, we also want to cover the top models being used, and their capabilities.

🚀The AI world just exploded with truly open-weight models in early 2026.
You can now download, run, fine-tune, and deploy frontier-level LLMs completely free (no API bills, full data privacy). These aren’t just chat toys — many are purpose-built for agentic systems (autonomous agents that use tools, plan multi-step tasks, call APIs, debug code, browse the web, etc.).

We’re talking Alibaba’s Qwen3.5 (the “alobab glm” you probably meant — Alibaba’s flagship is Qwen, while Zhipu AI’s is GLM-5), Google’s Gemma 4, Zhipu’s GLM-5, and more. All under permissive licenses (Apache 2.0 or MIT), available on Hugging Face, and runnable via Ollama (your favorite local tool).

Here’s the complete rundown: model sizes/storage, how to run them, and which ones crush agentic workflows (and why).

🧠 GLM-5 / GLM-5.1 — by Zhipu AI
Params: 744B total (40B active MoE)
License: MIT
Best For: Agentic engineering, coding, long-horizon tasks
Size: ~1.5 TB (BF16) → ~200–350 GB (4–8 bit quantized)
Multimodal: ✅ Yes (Text + Vision via GLM-5V)

⚡ Qwen 3.5–397B-A17B — by Alibaba
Params: 397B total (17B active MoE)
License: Apache 2.0
Best For: Multimodal agents, coding, long context (1M+ tokens)
Size: ~800 GB (BF16) → ~120–200 GB quantized
Multimodal: ✅ Yes (Text + Image + Audio)

💡 Gemma 4 31B — by Google
Params: 31B (dense)
License: Apache 2.0
Best For: On-device reasoning, coding, efficiency
Size: ~60 GB (BF16) → ~15–25 GB (4-bit)
Multimodal: ✅ Yes (Text + Image + Audio)

⚙️ Gemma 4 26B A4B — by Google
Params: 26B total (~4B active MoE)
License: Apache 2.0
Best For: Edge devices, high-speed agents
Size: ~50 GB (BF16) → ~8–15 GB quantized
Multimodal: ✅ Yes

🔥 DeepSeek-V3.2 — by DeepSeek
Params: 671B total (37B active)
License: Open
Best For: Math, reasoning, enterprise agents
Size: ~1.3 TB → ~150–250 GB quantized
Multimodal: ❌ Text only (but extremely strong reasoning)

🌊 Kimi K2.5 — by Moonshot AI
Params: ~1 Trillion total (32B active)
License: Open
Best For: Visual coding, agent swarms, advanced workflows
Size: Massive → ~100–180 GB quantized
Multimodal: ✅ Yes (vision-heavy)

Key notes on size & practicality:

Huge MoE models (GLM-5, Qwen3.5–397B, DeepSeek) only activate a fraction of parameters → much faster/cheaper than dense models of similar total size.

Quantization is your friend: Use 4-bit or 8-bit GGUF versions (via Ollama or llama.cpp). A 744B model drops from 1.5 TB to ~200 GB and runs on a single high-end GPU or even multi-GPU setups.

Smaller Gemma 4 variants run on a laptop or even phones (E2B/E4B are edge-optimized).

How to Use Them (Super Simple — Same as Before)
Option A: Ollama (easiest, recommended) You already know this from the Cloud guide. These are open-weights, so they run 100% locally (or via Ollama Cloud if you want zero hardware).

Bash

Pull & run (examples)

ollama pull glm-5:latest # or glm-5:4bit for smaller
ollama pull qwen3.5:397b-a17b
ollama pull gemma4:31b
ollama run glm-5
Works with your existing Python/JS/cURL scripts — just change the model name. No API key needed for local.

Option B: Hugging Face + vLLM / Transformers (production scale)

Python

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5", device_map="auto")

or use vLLM for insane speed on multiple GPUs

Option C: LM Studio / llama.cpp / OpenWebUI — GUI drag-and-drop for non-coders. All support GGUF quantized files → run 70B+ models on a MacBook or RTX 4090.

Free cloud options (if you don’t want local hardware):

Ollama Cloud (as we covered before) hosts many of these.
Fireworks, Together.ai, or Groq sometimes have free tiers for open models.
Hugging Face Inference Endpoints (pay-as-you-go but cheap for open models).
Best Models for Agentic Systems (2026 Winners)
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Agentic = models that reliably do tool calling, multi-step planning, long chains of actions, low hallucination, and persistent memory.

Top picks (ranked for real-world agents):

GLM-5 / GLM-5.1 (Zhipu AI) → Current king for agents
Why: Record-low hallucination, trained explicitly for long-horizon agentic tasks and complex systems engineering. Excels at tool use, code debugging, terminal control, and 200+ step workflows. Native function calling is rock-solid.
Use when: Building serious autonomous agents (OpenClaw-style, CrewAI, LangGraph). Many devs say it “just works” better than Qwen/Gemma for real execution.

1. Qwen3.5–397B-A17B (Alibaba) → Best multimodal agent

Why: Native vision + audio + 1M context. Insane at GUI automation, visual coding, and agent swarms. Hybrid thinking mode switches between fast and deep reasoning automatically.
Use when: Your agents need to “see” screenshots, process audio, or handle massive documents.

1. Gemma 4 31B / 26B A4B (Google) → Best efficiency + on-device agents

Why: Apache 2.0, multimodal from day one, excellent tool-calling support, and runs on laptops/phones. The MoE version gives huge-model quality at tiny-model speed.
Use when: Privacy-first or edge agents (no cloud, runs locally forever).
Honorable mentions:

DeepSeek-V3.2 → Math/reasoning beasts for scientific or financial agents.
Kimi K2.5 → Visual coding & agent swarms (can handle 200–300 sequential tool calls).
Why These Beat Everything Else for Agents
Native tool/function calling baked in during training (not just fine-tuned).
Low hallucination + long context = agents that actually finish tasks instead of looping or lying.
MoE efficiency = you get 700B+ level intelligence without needing a datacenter.
Fully open = fine-tune them yourself, deploy privately, no rate limits, no censorship.
Quick Start Recommendation
Just want to experiment? → ollama run gemma4:26b (runs on almost anything).
Building real agents? → Start with GLM-5 or Qwen3.5–397B (quantized). Pair with LangGraph, CrewAI, or Dify.
Zero hardware? → Use Ollama Cloud + your existing API key setup from last time.
All of these are free forever once downloaded. No subscriptions, no tokens to buy. Just pure open-source power.

If You’re Trying to Build With AI (Start Here)
I’m not just talking about this — I’m actively building systems, testing models, and documenting what actually works.

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